BE processing notebook update

jupyter_notebooks/BE_Processing.ipynb

@@ -58,12 +58,12 @@
 This notebook performs some functional fitting whose duration can be substantially decreased by using more memory and CPU cores. We have provided default values below but you may choose to change them if necessary.
 
 %% Cell type:code id: tags:
 
 ``` python
-max_mem         = 1024*2  # Maximum memory to use, in Mbs. Default = 1024
-max_cores       = 2       # Number of logical cores to use in fitting.  None uses all but 2 available cores.
+max_mem         = 1024*8  # Maximum memory to use, in Mbs. Default = 1024
+max_cores       = None    # Number of logical cores to use in fitting.  None uses all but 2 available cores.
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Make the data pycroscopy compatible
@@ -88,18 +88,10 @@
                                         file_filter='Parameters for raw BE data (*.txt *.mat *xls *.xlsx);; \
                                         Translated file (*.h5)')
 
 (data_dir, filename) = os.path.split(input_file_path)
 
-if copy_input_file:
-    _, ext = os.path.splitext(filename)
-    temp_path = os.path.join(data_dir, 'temp_file'+ext)
-    if os.path.exists(temp_path):
-        os.remove(temp_path)
-    shutil.copy2(input_file_path, temp_path)
-    input_file_path = temp_path
-
 if input_file_path.endswith('.h5'):
     # No translation here
     h5_path = input_file_path
     force = False # Set this to true to force patching of the datafile.
     tl = px.LabViewH5Patcher()
@@ -194,20 +186,20 @@
 
 h5_sho_group = px.hdf_utils.findH5group(h5_main, 'SHO_Fit')
 sho_fitter = px.BESHOmodel(h5_main, parallel=True)
 if len(h5_sho_group) == 0:
     print('No SHO fit found. Doing SHO Fitting now')
-    h5_sho_guess = sho_fitter.do_guess(strategy='complex_gaussian', processors=max_cores, options={'num_points':sho_fit_points})
-    h5_sho_fit = sho_fitter.do_fit(processors=max_cores)
+    h5_sho_guess = sho_fitter.do_guess(strategy='complex_gaussian', processors=max_cores, max_mem=max_mem, options={'num_points':sho_fit_points})
+    h5_sho_fit = sho_fitter.do_fit(processors=max_cores, max_mem=max_mem)
 else:
     print('Taking previous SHO results already present in file')
     h5_sho_guess = h5_sho_group[-1]['Guess']
     try:
         h5_sho_fit = h5_sho_group[-1]['Fit']
     except KeyError:
         print('Previously computed guess found. Now computing fit')
-        h5_sho_fit = sho_fitter.do_fit(processors=max_cores, h5_guess=h5_sho_guess)
+        h5_sho_fit = sho_fitter.do_fit(processors=max_cores, max_mem=max_mem, h5_guess=h5_sho_guess)
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Visualize the SHO results
@@ -376,11 +368,10 @@
 ``` python
 map_parm = 'Work of Switching'
 plot_cycle = 0
 plot_position = (int(pos_dims[0]/2), int(pos_dims[1]/2))
 plot_bias_step = 0
-h5_main.pos_dim_sizes
 px.viz.be_viz_utils.plot_loop_sho_raw_comparison(h5_loop_parameters, map_parm, plot_cycle, plot_position, plot_bias_step)
 
 ```
 
 %% Cell type:markdown id: tags:
@@ -391,7 +382,7 @@
 * Finally consider saving this notebook if necessary
 
 %% Cell type:code id: tags:
 
 ``` python
-hdf.close()
+# hdf.close()
 ```